Innodata Inc. (INOD) 2026 Q2 法說會逐字稿

Thank you for standing by. At this time, I would like to welcome everyone to the Innodata second-quarter 2026 earnings call. (Operator Instructions)

感謝您耐心等候。此刻，我想歡迎各位參加 In nodata 2026 年第二季財報電話會議。（接線員指示）

I would now like to turn the call over to Amy Agress. You may begin.

現在我想將電話交給 Amy Agress。您可以開始。

Thank you. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, Chairman and CEO of Innodata; Rahul Singhal, President and Chief Revenue Officer; and Jayant Chauhan, Chief Financial Officer. Also on the call today is Mariz Espineli, Chief Accounting Officer; and Aneesh Pendharkar, Senior Vice President, Finance and Corporate Development.

謝謝。各位下午好。感謝各位今天加入我們。今天的與會講者包括：Innodata 董事長兼執行長 Jack Abuhoff；總裁兼首席營收長 Rahul Singhal；以及財務長 Jayant Chauhan。今天同時在線的還有：會計長 Mariz Espineli；以及財務與企業發展資深副總裁 Aneesh Pendharkar。

We'll hear from Jack and Rahul first, who will provide perspective about the business and then Jayant will provide a review of our results for the second quarter. We'll then take questions from analysts.

我們將先聽 Jack 與 Rahul 發言，他們將就業務提供觀點，接著 Jayant 將回顧我們第二季的業績。之後我們將接受分析師提問。

Before we get started, I'd like to remind everyone that during this call we will be making forward-looking statements, which are predictions, projections or other statements about future events. These statements are based on current expectations, assumptions and estimates and are subject to risks and uncertainties. Actual results could differ materially from those contemplated by these forward-looking statements.

在開始之前，我想提醒各位，本次電話會議中我們將做出前瞻性陳述，也就是對未來事件的預測、推估或其他陳述。這些陳述係基於目前的預期、假設與估計，並受風險與不確定性影響。實際結果可能與這些前瞻性陳述所預期者有重大差異。

Factors that could cause these results to differ materially are set forth in today's earnings press release in the risk factors section of our Form 10-K, Forms 10-Q and other reports and filings with the Securities and Exchange Commission. We undertake no obligation to update forward-looking information.

可能導致結果出現重大差異的因素，已載明於今日財報新聞稿，以及我們 Form 10-K、Form 10-Q 的風險因素章節，與其他向美國證券交易委員會提交的報告與申報文件中。我們不承擔更新前瞻性資訊的任何義務。

In addition, during this call we may discuss certain non-GAAP financial measures. In our earnings release filed with the SEC today as well as in our other SEC filings, which are posted on our website, you will find additional disclosures regarding these non-GAAP financial measures, including reconciliations of these measures with comparable GAAP measures. Thank you.

此外，在本次電話會議中，我們可能會討論某些非 GAAP 財務衡量指標。在我們今日向 SEC 提交的財報新聞稿，以及其他已張貼於我們網站的 SEC 申報文件中，您將可找到關於這些非 GAAP 財務衡量指標的額外揭露資訊，包括與可比 GAAP 指標之間的調節表。謝謝。

I will now turn the call over to Jack.

現在我將把電話交給 Jack。

Thank you, Amy, and good afternoon, everyone. Q2 was another record quarter for Innodata. Revenue, adjusted gross profit, adjusted EBITDA and cash all reached new highs, and we exceeded analyst consensus on all key metrics. Revenue was $92.1 million, up 58% year over year, exceeding analyst consensus by approximately $5.8 million, or 7%, and making Q2 our 12th consecutive quarter of year-over-year growth. To put that in perspective, in Q2, as in Q1, our quarterly revenue exceeded our annual revenue of just three years ago.

謝謝你，Amy，各位下午好。第二季對 Innodata 而言是另一個創紀錄的季度。營收、調整後毛利、調整後 EBITDA 與現金水位皆創新高，且我們在所有關鍵指標上都超越分析師一致預期。營收為 9,210 萬美元，年增 58%，較分析師一致預期高出約 580 萬美元（約 7%），並使第二季成為我們連續第 12 個季度實現年對年成長。為了讓各位更有概念，第二季（如同第一季），我們的單季營收已超過僅三年前的全年營收。

Our adjusted gross margin, meanwhile, was 49%, up 2 points sequentially and 9 points above our 40% publicly stated target. Adjusted EBITDA was $25.4 million, up 92% year over year, exceeding analyst consensus by approximately $8.5 million, or 50%. Fully diluted earnings per share were $0.41 per share, nearly double analyst consensus of $0.21 per share. Again, this quarter, we delivered growth, margin expansion and cash generation together, while investing in innovation that converts to revenue within quarters, not years. That is the business model working as designed.

同時，我們的調整後毛利率為 49%，較前一季提升 2 個百分點，並較我們公開宣示的 40% 目標高出 9 個百分點。調整後 EBITDA 為 2,540 萬美元，年增 92%，較分析師一致預期高出約 850 萬美元（約 50%）。完全稀釋後每股盈餘為每股 0.41 美元，幾乎是分析師一致預期每股 0.21 美元的兩倍。本季我們再次同時交出成長、毛利率擴張與現金創造，並持續投資於能在「以季度而非以年」轉化為營收的創新。這正是商業模式按設計運作的結果。

Last quarter, we told you to expect our largest customer to represent a smaller percentage of total revenue. In Q2, our largest customer represented 37% of revenue, down from 56% of revenue in Q1, while the big tech customer we announced last quarter scaled from 17% of revenue to 34% of revenue, becoming our second-largest customer. While our largest customer contributed less revenue in Q2 than in Q1 as a result of a change in the quarter to program structure and service mix, we continue to expect it to grow year over year for the full year.

上季我們曾提到，預期最大客戶占總營收的比重將下降。第二季，我們最大客戶占營收 37%，低於第一季的 56%；而我們上季宣布的那家大型科技客戶，則由占營收 17% 擴大至 34%，成為我們第二大客戶。雖然由於本季方案結構與服務組合的變動，使最大客戶在第二季的營收貢獻低於第一季，但我們仍預期其全年將呈現年對年成長。

We also landed an important new customer in the quarter, one of the fastest-scaling frontier labs. The upshot is our base continues to [broaden] in both customers and customer programs.

本季我們也拿下了一位重要新客戶，屬於成長速度最快的前沿實驗室之一。總結而言，我們的客戶基礎在客戶數量與客戶專案兩方面都持續[擴大]。

Now before turning to guidance, I want to share an important announcement about Innodata's leadership. Effective September 30, Rahul Singhal will become President and Chief Executive Officer of Innodata and will join our Board, and I will transition into the role of Executive Chairman. This is a planned transition made from a position of strength. And for me, it is also a personal one.

在談到財測之前，我想分享一項關於 Innodata 領導團隊的重要公告。自 9 月 30 日起，Rahul Singhal 將出任 Innodata 總裁兼執行長，並加入董事會，而我將轉任執行董事長。這是一項在公司處於優勢地位下所規劃的交接。對我而言，這同時也是一項個人的決定。

Many of you know Rahul from these calls, from investor conferences and from the work he has led over the past several years as a principal architect of Innodata's transformation into a strategic partner to the world's leading AI builders. He knows our customers, he knows our technology and he knows our people.

許多各位透過這些電話會議、投資人會議，以及他過去數年所主導的工作而認識 Rahul；他是 Innodata 轉型為全球領先 AI 建構者之策略夥伴的主要設計者之一。他了解我們的客戶、了解我們的技術，也了解我們的人才。

Rahul has been central to every element of the strategy behind the results you have seen quarter after quarter. The Board and I didn't have to look far for the right leader. Rahul earned this role, taking on expanding responsibility year after year and delivering every time.

Rahul 一直是您所見到一季又一季成果背後策略的每個要素之核心。董事會與我不必費力尋找合適的領導者。Rahul 以年復一年承擔更大責任並次次交出成果，贏得了這個職位。

This is how we build this company: we grow our capabilities, and we promote our own people. As Executive Chairman, I will remain deeply engaged, focused on partnering with Rahul to build capabilities enabled by our research team. Bringing these capabilities to the federal government and to the enterprise, I believe, is where I can best contribute to creating significant shareholder value. And as one of the company's largest shareholders, that is exactly what I want to be doing.

這就是我們打造公司的方式：我們擴增能力，並提拔自家人才。作為執行董事長，我將持續深度參與，重點在於與 Rahul 合作，打造由我們研究團隊所賦能的能力。我相信，將這些能力帶入聯邦政府與企業端，是我能為創造顯著股東價值做出最佳貢獻之處。而作為公司最大股東之一，這正是我希望投入的方向。

Our work with the Mag 7 and the leading AI labs is on a firm path to greater heights and greater diversification. Our enterprise AI and federal strategies, built on the differentiated technology we develop for the frontier labs, represent opportunities for potentially driving high-quality recurring revenue that results in significant value creation.

我們與「Mag 7」以及領先 AI 實驗室的合作，正穩健邁向更高的成就與更高的多元化。我們的企業 AI 與聯邦策略，建立在我們為前沿實驗室所開發的差異化技術之上，代表著有機會推動高品質經常性收入、進而創造顯著價值的機會。

We are building Innodata to be a generational company. And with that same aspiration in mind, we were pleased to have announced recently that Jayant Chauhan joined Innodata as Chief Financial Officer. Jayant's abilities round out an already strong finance team, with Mariz Espineli stepping into the role of Chief Accounting Officer. Beyond the traditional CFO mandate, Jayant will work strategically on capital allocation and capital markets, customer partnerships, M&A that can accelerate our strategy and investor communications while scaling the financial infrastructure of the company we are becoming.

我們正將 Innodata 打造成一家可傳承世代的公司。秉持同樣的願景，我們也很高興近期宣布 Jayant Chauhan 加入 Innodata 擔任財務長。Jayant 的能力使我們原本就強大的財務團隊更臻完整，同時 Mariz Espineli 也接任會計長一職。除傳統財務長職責外，Jayant 將在資本配置與資本市場、客戶夥伴關係、可加速我們策略的併購（M&A）、以及投資人溝通等方面提供策略性支持，同時擴建我們正在成為的那家公司的財務基礎設施。

Before I turn the call over to Rahul, let me address guidance. We are reiterating our guidance of 40% or more year-over-year revenue growth. We have some large new potential engagements in our pipeline with both existing and new customers that we believe are likely wins, but we have not yet factored them at all into our forecast at this point. As a matter of prudence, we will only factor them into our forecast when we know they're 100% won, and we can forecast the timing of revenue recognition.

在我把電話交給 Rahul 之前，先談財測。我們重申指引：營收年對年成長 40% 或以上。我們的案源管線中，包含一些與既有及新客戶的大型潛在新合作案，我們認為很可能拿下，但目前尚未將其納入任何預測。基於審慎原則，只有在我們確認 100% 得標，且能預估營收認列時點後，才會將其納入預測。

I will now turn the call over to Rahul to discuss the market, our strategy, and the execution milestones that we believe prove the strategy is winning.

現在我將把電話交給 Rahul，請他談談市場、我們的策略，以及我們認為能證明策略正在奏效的執行里程碑。

Thank you, Jack, and good afternoon, everyone. Before I begin, a personal note. I'm truly honored by the confidence both Jack and the Board have placed in me, and I intend to repay it with results. Innodata has extraordinary momentum, an extraordinary team and an extraordinary opportunity in front of it. I intend to build on all three.

謝謝你，Jack，各位下午好。在我開始之前，先說幾句個人感言。我對 Jack 與董事會給予我的信任深感榮幸，我也打算以成果回報這份信任。Innodata 具備非凡的動能、非凡的團隊，以及擺在眼前的非凡機會。我將在這三方面持續再接再厲。

One of the most significant developments of the past 18 months is the increasingly pivotal role that research and innovation are playing at Innodata. It is not overstating the case to say that research has become a growth engine and the means by which we increasingly differentiate, expand existing partnerships and forge new customer relationships.

過去 18 個月最重大的發展之一，是研究與創新在 InnoData 所扮演的角色日益關鍵。毫不誇張地說，研究已成為成長引擎，也是我們日益用以差異化、擴大既有合作夥伴關係並建立新客戶關係的方式。

Our growth is increasingly driven by research and innovation across the full model training life cycle from pre-training and post training to model evaluation and benchmarking. Our innovation is producing intellectual property and differentiation that is generating demand.

我們的成長愈來愈由研究與創新所驅動，涵蓋完整的模型訓練生命週期，從預訓練、後訓練到模型評估與基準測試。我們的創新正在產生智慧財產與差異化，進而帶動需求。

Several quarters ago, we talked about how we were benchmarking frontier model performance: isolating weaknesses, building remediation datasets to address those weaknesses, and proving the efficacy of those datasets by training small models that were architecturally similar to the big ones. Today, we are doing much more than that.

幾個季度前，我們談到我們如何對前沿模型的效能進行基準測試：隔離弱點、建立修復資料集以解決這些弱點，並透過訓練在架構上與大型模型相似的小型模型來證明這些資料集的有效性。而今天，我們做的遠不止於此。

I'd like to share a few examples of what we are doing now, because the work is fascinating in its own right and because it gives you a sense of where we intend to take Innodata over the next several years.

我想分享幾個我們目前正在做的例子，因為這些工作本身就很引人入勝，也因為它能讓各位了解我們打算在未來幾年把 InnoData 帶往何處。

Through our research efforts, we established an early position in agentic reinforcement learning, one of the most important frontiers in AI development. With a large lab, we won a significant new program covering personalization of long-horizon agents, which is now scaling. And we have also been awarded a second program covering reinforcement learning environments for desktop computer-use agentic tasks.

透過我們的研究投入，我們在代理式強化學習（agentic reinforcement learning）上建立了早期領先地位，這是 AI 發展最重要的前沿之一。憑藉大型實驗室，我們贏得了一項重要的新專案，涵蓋長時域代理的個人化，目前正在擴大規模。此外，我們也獲得第二項專案，涵蓋用於桌面電腦使用之代理式任務的強化學習環境。

In the enterprise, we see companies quick to develop AI agents but struggling to deploy them in production with confidence. We believe combining our trusted observability platform and our innovatively architected reinforcement learning gyms enable us to position ourselves as the AI deployment assurance layer. We see this as opening a huge opportunity, and this is what Jack alluded to a few minutes ago.

在企業端，我們看到公司很快就能開發 AI 代理，但在有信心地將其部署到正式生產環境時卻面臨困難。我們相信，結合我們可信賴的可觀測性平台與創新架構的強化學習訓練場（gyms），能讓我們將自身定位為 AI 部署的保證層。我們認為這將開啟巨大的機會，這也是 Jack 幾分鐘前提到的。

In the quarter, we deepened delivery of these capabilities with one big tech customer and began delivery with another. This innovation has also opened active insurance and banking conversations that we expect to convert to pilots.

本季度，我們與一家大型科技客戶深化了這些能力的交付，並開始為另一家客戶提供交付。這項創新也促成了與保險與銀行業的積極洽談，我們預期可轉化為試點專案。

Frontier model builders have also become intensely focused on dynamic, long-horizon agentic evaluation. In the quarter, we released two public benchmarks, including one that tests how well models perform on multi-turn, long-context and multi-model interactions. A benchmark is an assembly of expert-authored prompts, rubric constraints and LLM judges configured to test frontier models. Both are designed to surface failure modes that standard leaderboards miss, things like rounding drift and instruction forgetting, precisely the failure modes frontier labs are working to improve. Each benchmark engagement results in a data strategy recommendation and sets us up to deliver scaled data generation to improve the model.

前沿模型建構者也開始高度聚焦於動態、長時域的代理式評估。本季度，我們發布了兩個公開基準，其中一個測試模型在多輪對話、長上下文與多模型互動上的表現。基準是由專家撰寫的提示（prompts）、評分規範約束（rubric constraints）以及配置用於測試前沿模型的 LLM 裁判（judges）所組成。兩者皆旨在揭露標準排行榜忽略的失敗模式，例如四捨五入漂移與指令遺忘，這些正是前沿實驗室致力改善的失敗模式。每一次基準合作都會產出資料策略建議，並讓我們得以提供規模化的資料生成，以提升模型。

In the quarter, we also expanded our capabilities in generating training data that extends the reasoning capabilities of the state-of-the-art models, delivering across five frontier labs and five domains. As AI moves from digital tasks to embodied intelligence, we are building the required data and measurement layer. This quarter, we signed two research agreements with a leading university and committed to a motion capture lab that we expect to come online in the next few months, capable of collecting sub-millimeter precision data for training robots and physical AI foundation models.

本季度，我們也擴大了生成訓練資料的能力，以延伸最先進模型的推理能力，並在五家前沿實驗室與五個領域交付成果。隨著 AI 從數位任務走向具身智慧（embodied intelligence），我們正在建構所需的資料與量測層。本季度，我們與一所頂尖大學簽署了兩項研究協議，並承諾建置一座動作捕捉實驗室，預計在未來幾個月上線，可收集次毫米精度資料，用於訓練機器人與實體 AI 基礎模型。

In the quarter, we ran successfully egocentric data collection pilots with leading robotics companies and our data collection practice shifted from individual pilots to scoping enterprise-scale multimodal programs, including a multilingual speech program spanning seven languages and a roughly 2 million-hour egocentric program that we hope to be awarded based on successful pilot results.

本季度，我們與領先的機器人公司成功執行了第一人稱（egocentric）資料收集試點，我們的資料收集業務也從單一試點轉向規劃企業級多模態專案，包括一個涵蓋七種語言的多語語音專案，以及一個約 200 萬小時的第一人稱專案；我們希望能基於成功的試點結果獲得該專案。

Data, data engineering and data science are central to improving AI and to making it safe and trustworthy. That centrality is what enables our research to deliver capabilities across many different spheres. Data engineering innovations can solve big AI challenges, including in domains where you might not expect to find us.

資料、資料工程與資料科學是提升 AI、並使其安全且值得信賴的核心。正因為這種核心地位，我們的研究才能在許多不同領域交付能力。資料工程的創新可以解決重大的 AI 挑戰，包括在一些你可能想不到我們會涉足的領域。

We mentioned one such domain in our Q4 call, how we had developed an AI model for drone and other small object detection that exceeds prior state-of-the-art benchmarks by 6.45% and how in a field where progress is often measured in fractions of a percentage point, a 6.45% improvement is a material advance. We are now working on demonstrating that capability to the government.

我們在第四季電話會議中提到其中一個領域：我們開發了一個用於無人機及其他小型物體偵測的 AI 模型，其表現較先前最先進基準高出 6.45%；而在一個進步常以百分之幾的零點幾來衡量的領域，6.45% 的提升是實質性的進展。我們目前正著手向政府展示這項能力。

Another example. As we announced earlier this week, we released the first stage of what we are calling our AI Cyber Training Suite. 12 datasets and evaluation systems that train AI coding agents to write secure code and to repair vulnerabilities in the company's existing software. When we tested leading open-weight models on their ability to repair verified flaws, the repair rate more than doubled after a single round of fine-tuning on just a portion of our data.

再舉一例。如同我們本週稍早宣布的，我們發布了我們所稱的 AI 資安訓練套件（AI Cyber Training Suite）的第一階段：12 個資料集與評估系統，用於訓練 AI 程式碼代理撰寫安全程式碼，並修補公司既有軟體中的弱點。當我們測試領先的開放權重模型在修復已驗證缺陷方面的能力時，僅在我們部分資料上進行一輪微調後，修復率就提升到原來的兩倍以上。

Given that AI now writes a growing share of the world's code, the inability to trust that code without the security team reviewing everything it produces is a real blocker to enterprise adoption. We believe our suite has the potential to remove that blocker.

鑑於 AI 現在撰寫了全球愈來愈高比例的程式碼，若無法在資安團隊逐一審查其產出之前就信任這些程式碼，將成為企業採用的真正阻礙。我們相信，我們的套件有潛力移除這個阻礙。

Across frontier labs, federal and the enterprise, the pattern is the same. Research and innovation are creating differentiated capabilities that win programs and compound into durable customer relationships. We couldn't be more excited about the opportunity ahead of us.

在前沿實驗室、聯邦政府與企業端，模式都是相同的。研究與創新正在創造具差異化的能力，贏得專案，並累積成為持久的客戶關係。我們對眼前的機會感到無比振奮。

Jack, back to you.

Jack，交回給你。

Thanks, Rahul. I also want to take a few minutes to connect this quarter's results to the structural economics of our business and to spend a few minutes talking about the broader market dynamics.

謝謝你，Rahul。我也想花幾分鐘把本季度的成果與我們業務的結構性經濟特徵連結起來，並再花幾分鐘談談更廣泛的市場動態。

First, operating leverage. Revenue grew 58% year over year, while adjusted EBITDA grew 92%, roughly 1.6 times faster. Each incremental program builds on the same core operating infrastructure, so the marginal cost of the next program is meaningfully lower than the cost of building that capability from scratch.

第一，營運槓桿。營收年增 58%，而調整後 EBITDA 年增 92%，約快 1.6 倍。每一個新增專案都建立在相同的核心營運基礎設施之上，因此下一個專案的邊際成本，明顯低於從零開始建置該能力的成本。

Second, margin quality. Adjusted gross margin of 49% is 9 points above our publicly stated target. The expansion is driven by mix and bolstered by the high-value pretraining programs and off-the-shelf datasets where we retain IP and monetize the same asset across multiple customers. These are the software-leveraged economics we have been deliberately building toward.

第二，毛利品質。調整後毛利率 49%，比我們公開宣示的目標高出 9 個百分點。此擴張由產品組合所驅動，並受高價值的預訓練專案與現成資料集所支撐；在這些業務中，我們保留 IP，並可將同一資產在多個客戶間變現。這正是我們刻意打造、以軟體槓桿為特徵的經濟模式。

Now, turning to the broader market dynamics. There are debates about whether we are at a peak AI CapEx, whether competition will commoditize models, and what the recent security incidents mean for the industry. Now, these debates play out against extraordinary numbers.

接著談更廣泛的市場動態。市場上有一些辯論：我們是否正處於 AI 資本支出（CapEx）的高峰、競爭是否會使模型商品化，以及近期的安全事件對產業意味著什麼。而這些辯論是在極為驚人的數字背景下展開的。

Hyperscaler capital spending is guided to roughly $700 billion this year, nearly double last year, with estimates revised upward throughout the year. But we believe each of these debates resolves in favor of the data evaluation and assurance layer we provide.

超大規模雲端業者（hyperscalers）今年的資本支出指引約為 7,000 億美元，幾乎是去年的兩倍，且全年估算持續上修。但我們相信，這些辯論最終都將導向有利於我們所提供的資料評估與保證層。

[Let me explain]. If CapEx comes under pressure, monetization pressure rises and monetization runs on deployment, fine-tuning and assurance, our business. If inference commoditizes, two things follow: labs engineer for use-case-specific differentiation, which requires specialized data; and AI becomes more accessible to the enterprise, which requires more assurance, not less. Again, our business.

[讓我解釋]。如果資本支出承壓，變現壓力就會上升，而變現依賴部署、微調與保證——也就是我們的業務。如果推論（inference）商品化，將帶來兩個結果：實驗室會為特定使用情境的差異化而進行工程設計，這需要專門化資料；而 AI 對企業更易取得，則需要更多而非更少的保證。同樣地，這也是我們的業務。

If security incidents multiply, they prove the need for exactly the engineering we announced this week. Yet again, our business. However, the market moves, we believe it moves toward the work that we do.

如果安全事件增加，它們將證明我們本週宣布的工程能力正是所需。又一次，是我們的業務。無論市場如何變動，我們相信它都會朝向我們所做的工作前進。

I will now turn the call over to Jayant, our new Chief Financial Officer, to walk through the financials.

接下來我將把電話會議交給我們的新任財務長 Jayant，由他帶領大家回顧財務表現。

Thank you, Jack, and good afternoon, everyone. I'm Jayant Chauhan, Innodata's Chief Financial Officer. And as you know, this is my first earnings call since coming on board in July. Mariz has transitioned into the role of Chief Accounting Officer, and I'm thankful to her for her partnership in getting me up to speed quickly.

謝謝你，Jack，各位下午好。我是 Jayant Chauhan，Innodata 的首席財務官。如各位所知，這是我自 7 月加入以來的第一次財報電話會議。Mariz 已轉任首席會計官一職，我也感謝她在協助我快速上手方面的合作與支持。

I've spent the past several weeks getting to know the business and meeting our teams here in the US and around the world. I'm energized by what I've found. I look forward to getting to know many of you on this call and afterwards. With that, let me walk through our second quarter results.

過去幾週我花了不少時間了解業務，並與我們在美國及全球各地的團隊會面。我對所看到的一切感到振奮。我也期待在本次電話會議以及會後，能與在座許多位進一步認識。接下來，讓我帶各位回顧我們第二季的業績表現。

Revenue for quarter two 2026 was $92.1 million, up 58% year over year and 2% sequentially, our 12th consecutive quarter of year-over-year growth. This exceeded analyst consensus by $5.8 million, or 7%. Adjusted gross profit was $45.4 million, representing adjusted gross margin of 49%. That was 2 percentage points higher than Q1 and 9 percentage points above our externally communicated 40% target. The improvement was driven by the mix shift towards higher-margin programs.

2026 年第二季營收為 9,210 萬美元，年增 58%，季增 2%，為我們連續第 12 個季度實現年對年成長。此數字較分析師一致預期高出 580 萬美元，約 7%。調整後毛利為 4,540 萬美元，調整後毛利率為 49%。較第一季高出 2 個百分點，也比我們對外溝通的 40% 目標高出 9 個百分點。改善主要來自產品組合轉向毛利率較高的專案。

Adjusted EBITDA was $25.4 million, or 27.5% of revenue, up 92% year over year. This exceeded analyst consensus of $16.8 million by approximately 50%. Net income for the quarter was $14.4 million, double the $7.2 million we reported in Q2 last year. Fully diluted earnings per share was $0.41, exceeding the consensus estimate of $0.21 by approximately 95%. Our effective tax rate for the quarter was approximately 18%, compared to our long-term target range of 23% to 25%. The lower tax rate was driven by tax benefits recognized this quarter.

調整後 EBITDA 為 2,540 萬美元，約占營收 27.5%，年增 92%。此數字較分析師一致預期的 1,680 萬美元高出約 50%。本季淨利為 1,440 萬美元，為去年第二季 720 萬美元的兩倍。完全稀釋後每股盈餘為 0.41 美元，較一致預期的 0.21 美元高出約 95%。本季有效稅率約為 18%，相較我們長期目標區間 23% 至 25%。較低的稅率主要來自本季認列的稅務利益。

Turning to the balance sheet. We ended the quarter with $250.4 million in cash and short-term investments. Excluding customer prepayments, which are a pass-through, our cash and short-term investments position was approximately $134 million, up $37 million sequentially. We remain undrawn against our Wells Fargo credit facility.

接著看資產負債表。本季末我們持有現金及短期投資 2.504 億美元。扣除屬於代收代付性質的客戶預付款後，我們的現金及短期投資約為 1.34 億美元，較上一季增加 3,700 萬美元。我們在富國銀行（Wells Fargo）的授信額度目前仍未動用。

Lastly, after market close today, we will file a prospectus supplement establishing an at-the-market equity program with Goldman Sachs as lead agent, alongside a broader syndicate. The program provides an efficient supplemental capital markets tool that we can use selectively and opportunistically.

最後，在今日收盤後，我們將提交一份招股說明書補充文件，建立一項由高盛（Goldman Sachs）擔任主辦代理、並搭配更廣泛承銷團的「隨市發行」（at-the-market, ATM）股權計畫。該計畫提供一項高效率的補充性資本市場工具，讓我們能在選擇性且把握時機的情況下運用。

Our balance sheet is strong, with cash and short-term investments of approximately $134 million, net of customer prepayments and has no debt outstanding at end of Q2. The program preserves optionality to support future growth initiatives, potential strategic opportunities and continued balance sheet strength as we scale.

我們的資產負債表相當穩健：扣除客戶預付款後，現金及短期投資約 1.34 億美元，且在第二季末沒有任何未償還債務。該計畫保留了彈性，可在我們擴張規模時，用於支持未來成長計畫、潛在策略性機會，以及維持資產負債表的強健。

With that, let me close. This was a good quarter for me to step into, and I'm looking forward to building on the growth and financial discipline this team has already established.

以上是我的報告。對我而言，這是一個很好的季度來接手，我也期待在團隊既有的成長動能與財務紀律基礎上持續推進。

With that, I'll turn it back to the operator. Operator, we are ready for questions.

接下來我把時間交回給主持人。主持人，我們已準備好回答問題。

(Operator Instructions) George Sutton, Craig-Hallum.

(主持人指示) George Sutton，Craig-Hallum。

First, congrats to Rahul and welcome to Jayant. Jack, I still hope to harass you with questions regularly.

首先，恭喜 Rahul，也歡迎 Jayant 加入。Jack，我仍然希望能經常用問題「騷擾」你。

So I'm curious if we can talk about the things that are not in your guidance. You mentioned some opportunities that aren't necessarily 100% booked yet, thus not in guidance.

我想談談一些沒有納入你們財測指引的事項。你提到有些機會尚未百分之百確定入帳，因此沒有納入指引。

Can you give us any bigger picture in terms of what some of those opportunities look like? And will you give us more regular updates, perhaps, than just the quarterly announcements?

你能否從更宏觀的角度，談談其中一些機會大概是什麼樣貌？另外，你們是否會比僅在每季公告時，更定期地提供更新？

Sure, George. Thank you. And needless to say, I look forward to your questions as often as you'd like to bring them to me.

當然可以，George。謝謝你。不用說，我也期待你想問多頻繁就多頻繁地把問題帶給我。

Yeah. No, we were thrilled with the quarter. Really, I think there were a lot of proof points laid down in the quarter and some of the things that we're learning as we go forward are as important to us as the financial signal that you're seeing today.

是的。我們對本季表現非常滿意。我認為本季確實建立了許多「驗證點」，而且我們在前進過程中所學到的一些事情，對我們的重要性不亞於你今天看到的財務訊號。

The innovation that we're accomplishing, that we're producing, is teaching us -- is laying out the direction for us. It's showing us that reliability in agentic enterprise AI can be engineered. It's showing us that the kinds of innovations that we're capable of creating and the difference that we can make by operating at the data engineering layer in kind of very random things. Drone detection, cybersecurity, these are just two examples.

我們正在完成、正在產出的創新，正在教導我們——也正在為我們勾勒方向。它讓我們看到：在代理式企業 AI（agentic enterprise AI）上的可靠性是可以被工程化打造的。它也顯示出我們有能力創造的創新類型，以及我們在資料工程層運作時，能在一些看似非常零散的領域帶來的差異。例如無人機偵測、資安，這只是兩個例子。

When I look at the set of opportunities we have, they run the gamut. And now I'm responding to your question about the things that are significant, some quite large things that are not in our guidance today. They run across our capabilities. There are things that are on the government side and the enterprise side. There are things that are on the frontier model side.

當我看我們目前的機會集合時，範圍非常廣。而我現在是在回應你關於「目前尚未納入指引、但相當重要、其中一些規模很大」的那些機會。它們橫跨我們的各項能力。有些在政府端，有些在企業端。也有一些是在前沿模型（frontier model）端。

A lot of the capabilities that we're demonstrating now in agentic AI, both deployment and training, are prominent in our pipeline. We're excited about it. But from a methodological perspective, we've maintained the discipline to count our chickens only once they're hatched. So we're looking forward to sharing more as we proceed through the second half of the year. We think it's going to be exciting.

我們目前在代理式 AI 的能力展示——無論是部署或訓練——在我們的商機管線中都相當突出。我們對此感到興奮。但從方法論角度來看，我們仍維持紀律：小雞孵出來之前不先算數。因此，我們期待在下半年推進的過程中分享更多。我們認為會很令人期待。

So the security incidents that we're starting to see in AI are obviously concerning and seem to have created a very nice new opportunity for you. I wondered if you can just walk through that and, obviously, if you can bring to bear the press release from a couple of days ago with some of your capabilities. What does that mean in terms of opportunity for you?

所以，我們開始看到 AI 的安全事件，顯然令人擔憂，也似乎為你們創造了一個非常好的新機會。我想請你說明一下這部分；另外，若能結合幾天前的新聞稿以及你們的一些能力來談更好。這對你們而言在機會面上代表什麼？

Yeah, good question. So I think when we look at the problems that the enterprise is having, they want to embrace agentic AI, but can they trust it? What are the reasons that they may not be able to trust it? And certainly, when they're reading about models escaping their sandboxes or gaining elite cyber capabilities when they escape containment and things like this, that becomes a real concern.

是的，問得好。我認為當我們看企業所面臨的問題時，他們想擁抱代理式 AI，但他們能信任它嗎？他們可能無法信任它的原因是什麼？當然，當他們讀到模型逃離沙箱、或在突破隔離後獲得高階網路攻擊能力等這類消息時，這就會成為真正的疑慮。

Now one of the reasons that concern exists is a lot of the frontier models that are capable of these cybersecurity disruptions were themselves built on training data that contained unpatched code. It's fascinating. So basically, if you can identify the things that went into their training data mix and you can build an agent that can detect those code aberrations, can detect the code that's been introduced even when patches were subsequently introduced. And then from that, if you can enable that AI to generalize to new novel threats, things that it hasn't seen and identify threats that are in the existing software, you've got a very capable set of technologies that enable the enterprise to more safely adopt AI.

而這種疑慮存在的原因之一，是許多具備造成這類資安破壞能力的前沿模型，本身是用包含未修補程式碼的訓練資料所建構的。這很耐人尋味。因此，基本上，如果你能辨識出其訓練資料組合中包含了哪些內容，並打造一個代理（agent）去偵測那些程式碼異常、偵測即使後續已導入修補仍被引入的程式碼，再進一步，如果你能讓該 AI 泛化到新的、前所未見的威脅，去辨識既有軟體中的威脅，那你就擁有一套非常有能力的技術，能讓企業更安全地採用 AI。

So we're having some interesting discussions about that. We think it's another example of the kinds of innovation that we're increasingly capable of.

因此，我們正在就此進行一些有意思的討論。我們認為這也是我們日益具備創新能力的另一個例證。

Got you. And just one other question. Obviously, we're seeing more federal government testing of models before they are released and a lot of it through red teaming. Can you just give us a sense of your involvement in the broader federal area?

了解。另外還有一個問題。顯然，我們看到聯邦政府在模型發布前進行更多測試，其中很多是透過紅隊演練（red teaming）。你能否讓我們了解一下你們在更廣泛的聯邦領域中的參與情況？

So there are a couple of things there. I think we're having a lot of interesting discussions with players in the government about how we can partner with them and where we can cooperate with them. We're also discussing things with agencies, the ability to be represented in the Tradewinds Marketplace is an accepted solution for different things is a huge opportunity, a huge advantage that we now have.

所以這裡有幾件事。我認為我們正與政府中的相關人士進行許多很有意思的討論，探討我們如何與他們建立夥伴關係，以及在哪些方面可以與他們合作。我們也在與各機構討論相關事項；能夠在 Tradewinds Marketplace 上被呈現，且作為不同需求的可接受解決方案，對我們而言是一個巨大的機會，也是我們如今擁有的一項重大優勢。

I think from a perspective of what will be the federal government's relationship with AI, there are two things there. There's first -- they're very much accelerating their ability to procure AI solutions and get the best. The other thing that we're seeing is the frontier model companies are inviting the government proactively to help them regulate the agency. So when you look at what the eventual need will be for things like benchmarks and evaluations and red teaming, we released two benchmarks this quarter that we think are very novel and very useful to deliver those kinds of things and evaluation work on behalf of the government is an opportunity that we're tracking.

我認為，從聯邦政府與 AI 的關係角度來看，有兩件事。第一，他們正在大幅加速採購 AI 解決方案、並取得最佳方案的能力。第二，我們看到前沿模型公司正主動邀請政府協助他們對相關機構進行監管。因此，當你看最終對基準測試、評估與紅隊測試等工作的需求時，我們本季發布了兩項我們認為非常新穎且非常有用的基準測試，用以交付這類工作；代表政府進行這些評估工作是一個我們正在追蹤的機會。

Allen Klee, Maxim Group.

Allen Klee，Maxim Group。

You mentioned one of the positives this quarter was a higher mix of higher-margin projects. I was wondering, should we think of this as a trend towards that? Or maybe that was just the mix this quarter and it may revert back to where it's historically been?

你提到本季的正面因素之一是高毛利專案的占比提高。我想請問，我們是否應該把這視為朝這個方向的趨勢？或者這只是本季的組合因素，之後可能會回到歷史水準？

Yeah. So it's a very good question. I'm going to answer it in the following way. I think it's both. And now let me explain what I mean by that.

是的。這是一個非常好的問題。我會用以下方式回答。我認為兩者皆是。接下來我解釋一下我的意思。

We do bid on work that has a lower gross margin than the one that you're seeing today. Some of those projects could be large. We would intend to take those on. If we win those, I think the cash flow from them will be -- we would anticipate to be quite compelling.

我們確實也會投標一些毛利率低於你今天看到水準的工作。其中有些專案可能規模很大。我們也打算承接這些專案。如果我們得標，我認為其現金流——我們預期——會相當有吸引力。

Would that mean the gross margin on a weighted basis would decline somewhat? It would. On the other hand, from a strategic perspective, the things that we're working on, the things we're innovating, will likely have a higher revenue quality. And we measure revenue quality at or we think of revenue quality as a function both of gross margin and the recurring nature of that revenue. So I think over time, strategically, it's going to trend upward. I think on a quarter-by-quarter basis, it will depend on product mix.

那是否意味著加權後的毛利率會有所下降？會的。但另一方面，從策略角度來看，我們正在做的事情、我們正在創新的事情，可能會帶來更高的營收品質。我們衡量營收品質，或我們對營收品質的理解，是毛利率與該營收的經常性（可重複性）兩者的函數。因此我認為，從長期策略來看，它會呈上升趨勢。但以每一季來看，則取決於產品組合。

That's helpful. Then also you talked about using off-the-shelf datasets more often to do the training. I'm just trying to -- could you explain a little of like do you own the data or you get to use it and use it multiple times? And if you don't do that, how you're accessing the data?

這很有幫助。另外你也談到更常使用現成資料集來做訓練。我想釐清一下——你們是擁有這些資料，還是取得使用權並可多次使用？如果不是這樣，你們又是如何取得資料的？

Sure. So the off-the-shelf [datasets] up until now, and I'll come back as to why I said that. For the most part, up until now, our datasets that we engineer, and we engineer them around model deficiencies that we detect in our benchmarking. So when we see that there is a deficiency or when we see that -- or when we identify a capability that the frontier models are looking to create, and we can engineer a dataset that helps them get there, rather than waiting for them to request that of us, we build that dataset, we maintain or we retain the IP associated with that dataset, and we enable them to use those datasets for training their models.

當然。關於現成的[資料集]，到目前為止——我等一下也會回到我為什麼這麼說。大多數情況下，直到目前為止，我們所工程化（打造）的資料集，是圍繞我們在基準測試中偵測到的模型缺陷來設計的。因此，當我們看到存在缺陷，或當我們看到——或當我們辨識到前沿模型正在尋求建立的一項能力，而我們可以工程化一個資料集來幫助他們達成時，我們不會等他們向我們提出需求；我們會先建置該資料集，並維持或保留與該資料集相關的智慧財產權（IP），同時讓他們能使用這些資料集來訓練他們的模型。

It's good for everybody, right? It's good for our customers and it's good for us. And that's one of the contributors to higher margin profiles.

這對所有人都有好處，對吧？對我們的客戶有利，對我們也有利。而這也是較高毛利結構的其中一個貢獻因素。

There are also times when, on behalf of someone else who owns a dataset, we will represent them. We will perhaps do some engineering to that data. We will configure it so that it's ready for models to be trained on it. And then we will invite our customer partners to utilize that data as well. But most of what you're seeing today is data that we've figured out how to assemble around particular model needs and frontier model capabilities.

也有一些時候，我們會代表擁有某個資料集的第三方。我們可能會對該資料做一些工程化處理。我們會進行配置，使其準備好可供模型訓練使用。接著我們也會邀請我們的客戶夥伴使用該資料。但你目前看到的大多數，都是我們已經找出如何圍繞特定模型需求與前沿模型能力來組裝的資料。

My last question is, in the most likely case scenario, is there any reason that it would be likely that there would be a sequential decline in revenues in the third or fourth quarter?

我最後一個問題是，在最可能的情境下，有沒有任何理由會使第三季或第四季的營收可能出現季減？

So within the constraints of our business model, it's certainly possible. And if it were to occur, I don't know that I would particularly care. So what I care mostly about is where we're taking the company and where it's going, not quarter-to-quarter performance.

在我們商業模式的限制條件下，這當然是有可能的。而如果真的發生，我不確定我會特別在意。我最在意的是我們要把公司帶往哪裡、公司將走向何處，而不是逐季的表現。

The kinds of innovations that we're producing today, the track record we're getting, the new customers that we're winning, I think over time, will continue to inure to our benefit. And I think that we're going to continue to grow this company in a very significant way over the next several years.

我們今天所產出的各類創新、我們正在建立的實績、我們正在贏得的新客戶，我認為隨著時間推移，將會持續對我們有利。而我也認為，在未來幾年，我們將以非常顯著的方式持續成長這家公司。

If we were to win a very large onetime project that were delivered in two quarters and then there were an air gap after a third quarter, would I consider that a failure? Not at all. What I would consider a failure is if we're not maintaining the relevance that we are right now to our customers and if we weren't identifying huge market opportunities that I believe we'll be able to exploit over the next several years.

如果我們贏得一個非常大型的一次性專案，在兩個季度內交付，然後第三季出現一段空窗期，我會把那視為失敗嗎？完全不會。我會認為失敗的是：如果我們無法維持目前對客戶的相關性，或如果我們沒有辨識到我相信我們能在未來幾年加以利用的巨大市場機會。

This concludes our question-and-answer session. I will now turn the call back over to Jack Abuhoff for closing remarks.

問答環節到此結束。我現在把電話交回給 Jack Abuhoff 作結語。

Thank you very much. So to wrap up, Q2 2026 was another record quarter for Innodata. It was an across-the-board beat. We delivered 58% revenue growth, 49% adjusted gross margin, 92% adjusted EBITDA growth and significant cash generation. It was our 12th consecutive quarter of year-over-year growth as well.

非常感謝。總結一下，2026 年第二季是 Innodata 再創紀錄的一季。我們全面超出預期。我們實現了 58% 的營收成長、49% 的調整後毛利率、92% 的調整後 EBITDA 成長，以及顯著的現金創造。這也是我們連續第 12 個季度實現年對年成長。

We're seeing that diversification is happening in practice. Our largest customer declined to 37% of revenue while our overall business grew. And the customer that generated essentially no revenue a year ago is now our second-largest customer.

我們看到多元化正在實際發生。在整體業務成長的同時，我們最大客戶的占營收比重下降至 37%。而一年前幾乎沒有帶來營收的那位客戶，現在已成為我們的第二大客戶。

We announced a planned leadership transition. Rahul will become our President and CEO on September 30. I'll become our Executive Chairman. I'll be focused on building long-term differentiating capabilities across our enterprise and federal markets. Meanwhile, Jayant Chauhan has joined as CFO, further strengthening our financial leadership and enabling me to do some of the things that I want to do.

我們宣布了一項規劃中的領導階層交接。Rahul 將於 9 月 30 日出任總裁兼執行長。我將擔任執行董事長。我將專注於在我們的企業與聯邦市場中，打造長期且具差異化的能力。同時，Jayant Chauhan 已加入擔任財務長（CFO），進一步強化我們的財務領導力，也讓我能去做一些我想做的事情。

As one of the company's largest shareholders, I believe this is a tremendous path forward to very significant shareholder value creation. As we've discussed, our growth is increasingly research-driven and innovative, from novel benchmarks and reinforcement learning environments to capabilities in agentic deployment assurance, physical AI.

作為公司最大的股東之一，我相信這是一條能夠創造非常可觀股東價值的絕佳前進道路。如同我們所討論的，我們的成長愈來愈由研究驅動並且富有創新，涵蓋從新穎的基準測試與強化學習環境，到代理式部署保證（agentic deployment assurance）、實體 AI 等能力。

I think we're at the very early stages of many of this. So we're very excited about what lies ahead. We're very confident that 2026 can be a tremendous year for Innodata, and I thank all of you for continuing to be on this journey with us.

我認為我們在其中許多方面都還處於非常早期的階段。因此我們對未來的發展感到非常興奮。我們非常有信心，2026 年將會是 Innodata 非常出色的一年，也感謝各位持續與我們一同走在這段旅程上。

Ladies and gentlemen, that concludes today's call. Thank you all for joining. You may now disconnect.

各位女士、先生，今天的電話會議到此結束。感謝各位參與。您現在可以掛線。